題目🏂🏼𓀈：Bio-inspired Approaches for Real-Time Navigation of
Mobile Robots in Unknown Environments
報告人： Simon X. Yang (楊先一) 教授🟣🦵🏽，加拿大Guelph大學
時間🚴🏿♀️：2014.6.16, 上午9:00
地點🚆：意昂3娱乐（新校）信息樓323
Simon X. Yang(楊先一)，本科畢業於北京大學🍞🧏♀️、碩士畢業於美國休斯頓大學（University of Huston）🧑🏿🦳、博士畢業於加拿大阿爾伯塔大學(University of Alberta)🦶🏼。現為加拿大Guelph大學高級機器人與智能系統實驗室主任，終身教授🕵️，博士生導師。
主要研究領域🖐🏿：移動機器人路徑規劃與控製、多傳感器信息融合、無線傳感器網絡、智能計算與優化、多機器人系統等#️⃣。國際雜誌《IEEE Transactions On Neural Networks》、《IEEE Transactions On Systems, Man, And Cybernetics, Part B》、《International Journal of Robotics and Automation》、《Control and Intelligent Systems》副主編; 國際雜誌《International Journal of Computational Intelligence and Applications》♝、《International Journal of Automation and Systems Engineering》👩🏻💼、《Journal of Robotics》、《International Journal of Computing and Information Technology》🩵、《International Journal of Information Acquisition》編委.
報告內容簡介：
Studyies of biologically inspired intelligent systems have been made significant progress in both understanding the biological intelligence and applying to various artificial engineering systems. In this talk, two algorithms for real-time navigation of mobile robots in unknown environments is presented. The first approach integrates a novel learning algorithm de-rived from Skinner’s operant conditioning and a shunting neural dynamics model, producing the capability of path planning in unknown and cluttered environments, after training and assistance with an angular velocity map. Second, a fuzzy logic based bio-inspired system is developed for mobile robot navigation. Based on a modified Braitenberg’s automata model, a bio-inspired hybrid fuzzy neural network structure is designed to control the robot, where the neural network weights are obtained from the fuzzy system. The effectiveness of both proposed methods are validated by simulation studies. In comparison to the Chang-Gaudiano algorithm under the same conditions, the proposed bio-inspired algorithm not only allows the robot to navigate efficiently in cluttered environments, but also significantly improves the computational and training time. This bio-inspired algorithm was successfully implemented on a real mobile robot for indoor obstacle avoidance.
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